RRepoGEO

REPOGEO REPORT · LITE

markovka17/dla

Default branch 2025 · commit 990f3605 · scanned 6/2/2026, 7:08:44 AM

GitHub: 749 stars · 121 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface markovka17/dla, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add an explicit introductory sentence to the README

    Why:

    COPY-PASTE FIX
    This repository, markovka17/dla, provides course materials for **Deep Learning for Audio (DLA)**, a lecture and seminar series focusing on deep learning applications in audio processing.
  • mediumtopics#2
    Add educational topics to better categorize the repository

    Why:

    CURRENT
    deep-learning, keyword-spotting, signal-processing, speaker-verification, speech-recognition, tts, voice-conversion
    COPY-PASTE FIX
    deep-learning, keyword-spotting, signal-processing, speaker-verification, speech-recognition, tts, voice-conversion, course, education, lecture-materials, seminar
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://www.hse.ru/en/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface markovka17/dla
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HuBERT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. HuBERT · recommended 2×
  2. OpenAI Whisper · recommended 2×
  3. PyTorch · recommended 1×
  4. torchaudio · recommended 1×
  5. Wav2Vec 2.0 · recommended 1×
  • CATEGORY QUERY
    How to get started with deep learning models for speech processing applications?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. torchaudio
    3. Wav2Vec 2.0
    4. HuBERT
    5. TensorFlow
    6. Keras
    7. Hugging Face Transformers
    8. Whisper
    9. Bark
    10. ESPnet
    11. SpeechBrain
    12. OpenAI Whisper

    AI recommended 12 alternatives but never named markovka17/dla. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep learning techniques for automatic speech recognition and audio signal processing?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper
    2. Google USM
    3. Conformer
    4. Listen, Attend and Spell (LAS)
    5. Deep Speech 2
    6. VGGish
    7. ResNet-based architectures
    8. WaveGAN
    9. Parallel WaveGAN
    10. wav2vec 2.0
    11. HuBERT
    12. XLS-R

    AI recommended 12 alternatives but never named markovka17/dla. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of markovka17/dla?
    pass
    AI named markovka17/dla explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts markovka17/dla in production, what risks or prerequisites should they evaluate first?
    pass
    AI named markovka17/dla explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo markovka17/dla solve, and who is the primary audience?
    pass
    AI named markovka17/dla explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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markovka17/dla — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite